My experience with numerical algorithms in Python has been a 10000x speedup and 10x memory reduction by using Numpy arrays and C/fortran functions. And when Numpy doesn't have a necessary function, a few extra lines of static C types and on-import compilation with Cython has still resulted in a 1000x speedup.
"Optimizing Python" needs to look at the whole existing ecosystem of C libraries - to be useful the end result has to be faster than what's possible now, not just faster than pure Python.
The speed improvements are worth it given how easy it is to use, I have seen literally a 1000x speedup on a time-series data analysis script from adding about 10 lines of static type annotations.
The functionality I like best about it are:
* It can be used transparently without any makefiles or compilation steps. Add "import pyximport; pyximport.install()" at the top of your main script, and now every imported Cython-capable module is compiled on the fly at runtime.
* You can start out with no changes to your python modules. All libraries and features still work within compiled modules. Then you can slowly start adding static types to a few variables at a time. The annotated variables become very fast native C integers/doubles/functions, instead of Python objects.
* Install it with pip, the packages in your OS distribution may be out of date,
* Rename your numeric code module's .py file to .pyx,
* Use pyximport from the main script ( http://docs.cython.org/src/userguide/source_files_and_compil...) to have it compiled at runtime without any extra build steps, and then
* Start experimenting with adding a few "cdef"s (http://docs.cython.org/src/quickstart/cythonize.html and http://docs.cython.org/src/tutorial/numpy.html)
I guess what I'm trying to say is, while this is a laudable effort, it seems what Python (and Ruby) really needs is a way to free itself from the chains of extension API compatibility.